The principal aim of the proposed work is to develop and bring to clinical trial a novel quantitative ultrasound (US) method that uses a texture stochastic modeling of radio frequency (RF) data, to quantify and classify the different pathologies of the human tissue, for example, liver, prostate, and breast tissue. In this project a new model for the backscatter RF echo (speckle texture) is proposed that takes into account the imaging as well as the physical interaction between the tissue and the tissue and the incoming pulse. The RF speckle model is a Gaussian Markov Random Field model that appears to capture the speckle structure well, is consistent with the first order marginal statistics of the complex amplitude speckle, and explains the homogeneous coarseness appearance of the speckle in terms of a homogeneous colored random field with a definite correlation structure. The goodness of the model and the underlying assumption are tested using a well established statistical test. This modeling approach provides a coherent theoretical basis for the design of robust and powerful detection schemes for tumor detection and possible objective classification. In addition, it allows for the receiver operating characteristics curve (ROC) to be derived, and an objective measure of the goodness of the classifier to be obtained. This work attempts to relate the model parameters to the physical tissue microstructure information such as the degree of regularity or the lack of it, the scatterer spacing (or average scatterer spacing) and arrangement, the effective scatterers density number (ESND), etc. A formal test has been proposed to check whether a given RF image exhibits any regularity. Moreover, a statistically efficient way (maximum likelihood estimation) of computing the scatterer spacing is proposed. A new measure of the effective scatterer number density which is based on the second-order statistical measure (e.g. the power spectral density) rather than on the first-order statistical measure (e.g. Kurtosis) is proposed. A Bayesian fusion center that combines the information obtained from different indices such as the 'backscatter' modeling, the scattering spacing and arrangements, the effective scatterer number density, the attenuation measurements, the scaling measurements, the first order moments, and attenuation maps and modeling to arrive at global and more reliable decision for tissue characterization and tumor detection, is proposed. The ultrasonography of the organs chosen for this study have been an active area of clinical research in the last decade. The attractiveness of the suggested organs lies in the fact that all the cases to be studied will have a full clinical evaluation (prior to surgery) and a thorough pathological analysis. This should have a great impact on the validation and confirmation of our results.